Welding processes are routinely used for joining pieces of metal in the manufacturing and construction industries, including the aerospace, automotive, and electronics industries. These welding processes typically use electric arcs or energy beams as heat sources. However, welding processes are still unable to be precisely controlled to reliably produce quality welds or weld profiles. In many applications, a higher level of process control is required and, as such, feedback from the process can be vital to properly regulate welding conditions so that the desired process control can be achieved.
Currently, such process control is largely accomplished through control of so-called primary process variables. As an example, for an arc welding process, primary process variables can include arc current, arc voltage, physical arc length, arc pulse duration and pulse characteristics (either in the voltage or current waveforms), workpiece travel speed or rotational speed (e.g., for round parts), wire feed rate or rate of material addition, preheat of the part, and gas flow and type of welding gas. As another example, for energy beam based processes, primary process variables can include beam power and focal characteristics for the energy beam, workpiece travel speed or rotational speed (for round parts), wire feed rate or rate of material addition, preheat of the part, continuous versus pulsed mode (including pulse rate and shape), gas flow and type of welding gas for laser beam processes, and beam Lissajous patterns formed by deflection coils and scanning frequency of these Lissajous patterns for electron beam processes.
As shown schematically in FIG. 1, the control of these primary welding machine variables provides only indirect control over the welding process, including weld pool, the weld pool volume and shape, the heat flow in the part, and the metallurgical properties of the final component. Occasionally, there can be some feedback from the welding process itself, as shown schematically in FIG. 2. However, in general, there is not at present a direct method for sensing the volume of the weld pool and an associated control scheme that directly regulates the volume of the weld pool.
There have been several previous efforts in thermal sensing and control of welding processes over the years. These previous efforts are described in the references below, each of which is incorporated herein by reference.
Z. Bingul, G. E. Cook, and A. M. Strauss, “The application of fuzzy logic to spatial thermal control in fusion welding,” Industry Applications, IEEE Transactions 36(6), 1523 (2000) considers the problem of sensing and controlling torch position in a pulsed gas metal arc welding (P-GMAW) process. To deal with the nonlinear time-varying process with its inherent stochastic disturbances associated with metal transfer, the theory of fuzzy sets was used as a general framework to interpret the uncertain arc signals and provide logic for control. A fuzzy logic controller weld joint tracking system was implemented and tested with pulsed gas metal arc welds under a variety of conditions.
Fredrik Sikström et al., “Modelling and simulation for feedback control of welding,” Proc. of the 18th IASTED Int. Conf: modelling and simulation, Montreal, Canada, 131 (2007) describes a promising strategy that makes use of modelling and simulation to support design of real time controller in automated welding. A finite element method was used for thermal modelling of gas tungsten arc welding on a simplified test object. Data for model calibration and validation was acquired through thermal imaging during weld experiments on test objects of SS316 alloy. An optimization scheme for inverse modeling was employed in the calibration of the distributed weld process model. Frequency weighted model reduction and parametric system identification were applied and evaluated to obtain a low-order model of the single-input, single-output dynamics between a simulated weld source (actuator) and a simulated sensor signal. This low order model was then used for controller design and the closed-loop performance was evaluated by simulation of the weld process model.
G. Korizis and C. Doumanidis, “Scan welding: Thermal modeling and control of material processing,” J. Manufacturing Science and Engineering 121(3), 417 (1999) provides a thermal analysis of scan welding, as a redesign of classical joining methods, employing computer technology to ensure the composite morphologic, material and mechanical integrity of the joint. Real-time control of the welding temperature field was obtained using a proper dynamic heat input distribution on the weld surface. This distribution was implemented in scan welding with a single torch that swept the joint surface by a controlled, reciprocating motion, and adjusting power by in-process feedback of infrared temperature measurements. An off-line numerical simulation of the thermal field in scan welding was established, as well as a linearized multivariable model with real-time parameter identification. An adaptive thermal control scheme was implemented and validated both computationally and experimentally on a robotic gas-tungsten arc welding setup. The resulting productivity and quality features of scan welding were comparatively analyzed in terms of material structure and properties of the joint.
Fabrice Bardin et al., “Process control of laser conduction welding by thermal imaging measurement with a color camera,” Applied Optics 44(32), 6841 (2005) considers conduction welding as an alternative to keyhole welding. Compared with keyhole welding, conduction welding is an intrinsically stable process because vaporization phenomena are minimal. However, as with keyhole welding, an on-line process-monitoring system is advantageous for quality assurance to maintain the required penetration depth which, in conduction welding, is more sensitive to changes in heat sinking. The maximum penetration is obtained when the surface temperature is just below the boiling point, and so it is desired to maintain the temperature at this level. Therefore, a two-color optical system was developed for real-time temperature profile measurement of the conduction weld pool. The key feature of the system was the use of a complementary metal-oxide semiconductor standard color camera leading to a simplified low-cost optical setup. The real-time temperature measurement and control performance of the system was described when a defocused beam from a high power Nd:YAG laser was used on 5-mm-thick stainless steel workpieces.
W. Lu et al., “Nonlinear interval model control of quasi-keyhole arc welding process,” Automatica 40(5), 805 (2004) addresses the development of a nonlinear model based interval control system for a quasi-keyhole arc welding process, a novel arc welding process which has advantages over laser welding and conventional arc welding processes. The structure of the nonlinear model chosen was based on an analysis of the quasi-keyhole process to be controlled. Because of variations in the manufacturing conditions, the parameters of the nonlinear model were uncertain but bounded by fixed intervals if the range of the manufacturing conditions was specified. To determine the intervals, extreme operating conditions/parameters (manufacturing conditions) were used to conduct experiments. Each experiment gave a set of model parameters and the interval for each parameter was given by the minimum and maximum among the values obtained from different experiments. Closed-loop control experiments verified the effectiveness of the developed system as a robust control which required no re-adjustment and could function properly when fluctuations/variations in manufacturing conditions, and thus the process dynamics, change, vary, or fluctuate.
Hua Zhang et al., “The real-time measurement of welding temperature field and closed-loop control of isotherm width,” Science in China Series E: Technological Sciences 42(2), 129 (1999) describes the real-time measurement of a welding temperature field by a colorimetric method. With the data acquired from it a closed-loop control system of the parameters of the temperature field were developed and tested. Experimental results proved that the system had high measurement speed (time of a field within 0.5 s) and good dynamic response quality. Weld penetration could be controlled satisfactorily under unstable welding condition.
Doumanidis, “Scan welding method and apparatus,” U.S. Pat. No. 5,552,575, describes a single weld head, or torch, that is time-shared to implement any specified distributed heat input by scanning along a weld centerline and a region surrounding the centerline while adjusting the torch intensity accordingly on its path. The scan welding torch reciprocates rapidly on dynamically scheduled trajectories while power to the torch can be modulated in real-time to provide a regulated heat input distribution in the weld region and on the weld centerline. The method can generate a smooth and uniform temperature field, and deposit the full length of the weld bead simultaneously at a controlled solidification rate. As a result, grain interlacing on the bead interface in conjunction with a regulated material microstructure can yield improved tensile joint strength.
However, none of the present processes are able to provide the control of the size and shape of the weld pool that is desired for some applications.